Browsing by Subject "AI"

Now showing 1 - 7 of 7
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    AI Based Modelling and Optimization of Turning Process
    (2012-08) Kulkarni, Ruturaj Jayant; El-Mounayri, Hazim; Anwar, Sohel; Wasfy, Tamer
    In this thesis, Artificial Neural Network (ANN) technique is used to model and simulate the Turning Process. Significant machining parameters (i.e. spindle speed, feed rate, and, depths of cut) and process parameters (surface roughness and cutting forces) are considered. It is shown that Multi-Layer Back Propagation Neural Network is capable to perform this particular task. Design of Experiments approach is used for efficient selection of values of parameters used during experiments to reduce cost and time for experiments. The Particle Swarm Optimization methodology is used for constrained optimization of machining parameters to minimize surface roughness as well as cutting forces. ANN and Particle Swarm Optimization, two computational intelligence techniques when combined together, provide efficient computational strategy for finding optimum solutions. The proposed method is capable of handling multiple parameter optimization problems for processes that have non-linear relationship between input and output parameters e.g. milling, drilling etc. In addition, this methodology provides reliable, fast and efficient tool that can provide suitable solution to many problems faced by manufacturing industry today.
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    AI on the Edge with CondenseNeXt: An Efficient Deep Neural Network for Devices with Constrained Computational Resources
    (2021-08) Kalgaonkar, Priyank B.; El-Sharkawy, Mohamed A.; King, Brian S.; Rizkalla, Maher E.
    Research work presented within this thesis propose a neoteric variant of deep convolutional neural network architecture, CondenseNeXt, designed specifically for ARM-based embedded computing platforms with constrained computational resources. CondenseNeXt is an improved version of CondenseNet, the baseline architecture whose roots can be traced back to ResNet. CondeseNeXt replaces group convolutions in CondenseNet with depthwise separable convolutions and introduces group-wise pruning, a model compression technique, to prune (remove) redundant and insignificant elements that either are irrelevant or do not affect performance of the network upon disposition. Cardinality, a new dimension to the existing spatial dimensions, and class-balanced focal loss function, a weighting factor inversely proportional to the number of samples, has been incorporated in order to relieve the harsh effects of pruning, into the design of CondenseNeXt’s algorithm. Furthermore, extensive analyses of this novel CNN architecture was performed on three benchmarking image datasets: CIFAR-10, CIFAR-100 and ImageNet by deploying the trained weight on to an ARM-based embedded computing platform: NXP BlueBox 2.0, for real-time image classification. The outputs are observed in real-time in RTMaps Remote Studio’s console to verify the correctness of classes being predicted. CondenseNeXt achieves state-of-the-art image classification performance on three benchmark datasets including CIFAR-10 (4.79% top-1 error), CIFAR-100 (21.98% top-1 error) and ImageNet (7.91% single model, single crop top-5 error), and up to 59.98% reduction in forward FLOPs compared to CondenseNet. CondenseNeXt can also achieve a final trained model size of 2.9 MB, however at the cost of 2.26% in accuracy loss. Thus, performing image classification on ARM-Based computing platforms without requiring a CUDA enabled GPU support, with outstanding efficiency.
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    Does your AI discriminate?
    (2020-05-15) Magid Manning, Julie; Kelley School of Business - Indianapolis
    My research indicates that relying on data analytics to eliminate human bias in choosing leaders won’t help.
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    Examining the Impacts of Robot Service on Hotel Guest Experience
    (2021-05) Jain, Namrata Rajendra Kumar; Liu-Lastres, Bingjie; Fu, Yao-Yi; Mirehie, Mona
    The aim of the study is to assess the impact of robot service on hotel guest experiences. Application of technology in tourism and hospitality services is growing each day. Using robots in hospitality establishment is becoming more and more popular, mainly because it can help cut down the labor costs, increase efficiency and reduce human contacts. Very few studies, however, have been done on examining customer experience regarding robots used in the hotel. Social media sites such as TripAdvisor are popular platforms where people share their first-hand experiences. Hence, this study focuses on studying the reviews of robotic hotels. Using the software Leximancer, reviews were studied and categorized in different themes to understand if the presence of the robot would create positive or negative experience for customers. The sample of the study included total of 2383 reviews related to robotic hotels from TripAdvisor from January 2011 to October 2020. The findings highlighted the major themes as Room, Robot, Hotel and Staff and their relationship with the ratings. It also provided insights into the contribution of robot service to consumer’s hotel experiences.
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    Fuzzy Controller Algorithm for Automated HVAC Control
    (IAARC, 2020-10) Chae, Myungjin; Kang, Kyubyung; Koo, Dan D.; Oh, Sukjoon; Chun, Jae Youl; Engineering Technology, School of Engineering and Technology
    This research presents the design framework of the artificial intelligent algorithm for an automated building management system. The AI system uses wireless sensor data or IoT (Internet of Things) and user's feedback together. The wireless sensors collect data such as temperature (indoor and outdoor), humidity, light, user occupancy of the facility, and Volatile Organic Compounds (VOC) which is known as the source of the Sick Building Syndrome (SBS) or New Building Syndrome because VOC are often found in new buildings or old buildings with new interior improvement and they can be controlled and reduced by appropriate ventilation efforts. The collected data using wireless sensors are post-processed to be used in the neural network, which is trained in accordance with the collected data pattern. When the users of the facility have the control of the building's ventilation system and the AI system is fully trained using the user input, it will mimic the user's pattern and control the building system automatically just as the user wants. In this research, data were collected from 4 different buildings: university library, university cafeteria, a local coffee shop, and a residential house. Fuzzy logic controller is also developed for better performance of the HVAC. Indoor air quality, temperature (indoor and outdoor), HVAC fan speed and heater power are used for fuzzified output. As a result, the framework and simulation model for the energy efficient AI controller has been developed using fuzzy logic controller and the neural network-based energy usage prediction model.
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    Global Translation of Machine Learning Models to Interpretable Models
    (2021-12) Almerri, Mohammad; Ben Miled, Zina; Christopher, Lauren; Salama, Paul
    The widespread and growing usage of machine learning models, especially in highly critical areas such as law, predicate the need for interpretable models. Models that cannot be audited are vulnerable to inheriting biases from the dataset. Even locally interpretable models are vulnerable to adversarial attack. To address this issue a new methodology is proposed to translate any existing machine learning model into a globally interpretable one. This methodology, MTRE-PAN, is designed as a hybrid SVM-decision tree model and leverages the interpretability of linear hyperplanes. MTRE-PAN uses this hybrid model to create polygons that act as intermediates for the decision boundary. MTRE-PAN is compared to a previously proposed model, TRE-PAN, on three non-synthetic datasets: Abalone, Census and Diabetes data. TRE-PAN translates a machine learning model to a 2-3 decision tree in order to provide global interpretability for the target model. The datasets are each used to train a Neural Network that represents the non-interpretable model. For all target models, the results show that MTRE-PAN generates interpretable decision trees that have a lower number of leaves and higher parity compared to TRE-PAN.
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    Understanding Barriers to Medical Instruction Access for Older Adults: Implications for AI-Assisted Tools
    (ACM, 2020-09) Karimi, Pegah; Martin-Hammond, Aqueasha; Human-Centered Computing, School of Informatics and Computing
    Recalling medical instructions provided during a doctor's visit can be difficult due to access barriers, primarily for older adults who visit doctors multiple times per year and rely on their memory to act on doctor's recommendations. There are several interventions that aid patients in recalling information after doctors' visits; however, some have been proven ineffective, and those that are effective can present additional challenges for older adults. In this paper, we explore the challenges that older adults with chronic illnesses face when collecting and recalling medical instructions from multiple doctors' visits and discuss implications for AI-assisted tools to enable older adults better access medical instructions. We interviewed 12 older adults to understand their strategies for gathering and recalling information, the challenges they face, and their opinions about automatic transcription of their conversations with doctors to help them recall information after a visit. We found that participants face accessibility challenges such as hearing information and recalling medical instructions that require additional time or follow-up with the doctor. Therefore, patients saw potential value for a tool that automatically transcribes and helps with recall of medical instructions, but desired additional features to summarize, categorize, and highlight critical information from the conversations with their doctors.